Overview

Dataset statistics

Number of variables23
Number of observations52
Missing cells393
Missing cells (%)32.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.5 KiB
Average record size in memory186.5 B

Variable types

Numeric17
Unsupported3
Categorical3

Alerts

Nuclear has constant value "402.79"Constant
Natural gas has 29 (55.8%) missing valuesMissing
Hydroenergy has 52 (100.0%) missing valuesMissing
Nuclear has 51 (98.1%) missing valuesMissing
Firewood has 46 (88.5%) missing valuesMissing
Sugarcane and products has 52 (100.0%) missing valuesMissing
Other Primary_x000d_ has 52 (100.0%) missing valuesMissing
Electricity has 6 (11.5%) missing valuesMissing
Kerosene/jet fuel has 11 (21.2%) missing valuesMissing
Diesel oil has 2 (3.8%) missing valuesMissing
Fuel oil has 1 (1.9%) missing valuesMissing
Charcoal has 34 (65.4%) missing valuesMissing
Gases has 40 (76.9%) missing valuesMissing
Other secondary has 17 (32.7%) missing valuesMissing
Year is uniformly distributedUniform
Year has unique valuesUnique
Oil has unique valuesUnique
Coal has unique valuesUnique
Total Primaries has unique valuesUnique
LPG has unique valuesUnique
Gasoline/alcohol has unique valuesUnique
Coke has unique valuesUnique
Total Secundaries has unique valuesUnique
Total has unique valuesUnique
Hydroenergy is an unsupported type, check if it needs cleaning or further analysisUnsupported
Sugarcane and products is an unsupported type, check if it needs cleaning or further analysisUnsupported
Other Primary_x000d_ is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-07-30 07:28:12.220796
Analysis finished2023-07-30 07:29:25.382716
Duration1 minute and 13.16 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Year
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1995.5
Minimum1970
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size548.0 B
2023-07-30T07:29:25.615995image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1970
5-th percentile1972.55
Q11982.75
median1995.5
Q32008.25
95-th percentile2018.45
Maximum2021
Range51
Interquartile range (IQR)25.5

Descriptive statistics

Standard deviation15.154757
Coefficient of variation (CV)0.0075944662
Kurtosis-1.2
Mean1995.5
Median Absolute Deviation (MAD)13
Skewness0
Sum103766
Variance229.66667
MonotonicityStrictly increasing
2023-07-30T07:29:25.921302image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1970 1
 
1.9%
1971 1
 
1.9%
1998 1
 
1.9%
1999 1
 
1.9%
2000 1
 
1.9%
2001 1
 
1.9%
2002 1
 
1.9%
2003 1
 
1.9%
2004 1
 
1.9%
2005 1
 
1.9%
Other values (42) 42
80.8%
ValueCountFrequency (%)
1970 1
1.9%
1971 1
1.9%
1972 1
1.9%
1973 1
1.9%
1974 1
1.9%
1975 1
1.9%
1976 1
1.9%
1977 1
1.9%
1978 1
1.9%
1979 1
1.9%
ValueCountFrequency (%)
2021 1
1.9%
2020 1
1.9%
2019 1
1.9%
2018 1
1.9%
2017 1
1.9%
2016 1
1.9%
2015 1
1.9%
2014 1
1.9%
2013 1
1.9%
2012 1
1.9%

Oil
Real number (ℝ)

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25844.853
Minimum7359.21
Maximum51000.36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size548.0 B
2023-07-30T07:29:26.182745image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum7359.21
5-th percentile8510.0205
Q118389.132
median25998.64
Q332056.952
95-th percentile43734.817
Maximum51000.36
Range43641.15
Interquartile range (IQR)13667.82

Descriptive statistics

Standard deviation10619.848
Coefficient of variation (CV)0.41090764
Kurtosis-0.40300171
Mean25844.853
Median Absolute Deviation (MAD)7440
Skewness0.24656818
Sum1343932.4
Variance1.1278116 × 108
MonotonicityNot monotonic
2023-07-30T07:29:26.435349image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17845.05 1
 
1.9%
20434.56 1
 
1.9%
29276.6 1
 
1.9%
24454.46 1
 
1.9%
20739.23 1
 
1.9%
21824.74 1
 
1.9%
20123.38 1
 
1.9%
18507.33 1
 
1.9%
23400.9 1
 
1.9%
18398.12 1
 
1.9%
Other values (42) 42
80.8%
ValueCountFrequency (%)
7359.21 1
1.9%
7606.95 1
1.9%
8132.55 1
1.9%
8818.86 1
1.9%
9022.9 1
1.9%
9776.21 1
1.9%
15614.64 1
1.9%
17315.63 1
1.9%
17360.97 1
1.9%
17695.37 1
1.9%
ValueCountFrequency (%)
51000.36 1
1.9%
45810.67 1
1.9%
44311.25 1
1.9%
43263.19 1
1.9%
41767.38 1
1.9%
41477.17 1
1.9%
40944.02 1
1.9%
37389.46 1
1.9%
36528.48 1
1.9%
35077.19 1
1.9%

Natural gas
Real number (ℝ)

Distinct23
Distinct (%)100.0%
Missing29
Missing (%)55.8%
Infinite0
Infinite (%)0.0%
Mean8955.0143
Minimum339.63
Maximum16958.96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size548.0 B
2023-07-30T07:29:26.663330image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum339.63
5-th percentile2080.811
Q17221.335
median9071.37
Q310698.23
95-th percentile16030.71
Maximum16958.96
Range16619.33
Interquartile range (IQR)3476.895

Descriptive statistics

Standard deviation4241.2674
Coefficient of variation (CV)0.47361927
Kurtosis-0.0054851062
Mean8955.0143
Median Absolute Deviation (MAD)1972.35
Skewness0.014881667
Sum205965.33
Variance17988349
MonotonicityNot monotonic
2023-07-30T07:29:26.865697image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
339.63 1
 
1.9%
8436.85 1
 
1.9%
8606.69 1
 
1.9%
9301.31 1
 
1.9%
9410.25 1
 
1.9%
10294.56 1
 
1.9%
16157.95 1
 
1.9%
16958.96 1
 
1.9%
14885.55 1
 
1.9%
11570.42 1
 
1.9%
Other values (13) 13
25.0%
(Missing) 29
55.8%
ValueCountFrequency (%)
339.63 1
1.9%
1877.29 1
1.9%
3912.5 1
1.9%
4436.21 1
1.9%
4711.77 1
1.9%
7099.02 1
1.9%
7343.65 1
1.9%
7898.26 1
1.9%
8436.85 1
1.9%
8592.96 1
1.9%
ValueCountFrequency (%)
16958.96 1
1.9%
16157.95 1
1.9%
14885.55 1
1.9%
14796.23 1
1.9%
11570.42 1
1.9%
11101.9 1
1.9%
10294.56 1
1.9%
9961.39 1
1.9%
9410.25 1
1.9%
9301.31 1
1.9%

Coal
Real number (ℝ)

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8519.1935
Minimum1155.91
Maximum14229.38
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size548.0 B
2023-07-30T07:29:27.099979image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1155.91
5-th percentile1259.8565
Q13610.845
median9967.74
Q312151.827
95-th percentile13887.299
Maximum14229.38
Range13073.47
Interquartile range (IQR)8540.9825

Descriptive statistics

Standard deviation4460.1068
Coefficient of variation (CV)0.52353627
Kurtosis-1.3250626
Mean8519.1935
Median Absolute Deviation (MAD)3326.77
Skewness-0.45718813
Sum442998.06
Variance19892552
MonotonicityNot monotonic
2023-07-30T07:29:27.357259image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1358.47 1
 
1.9%
1155.91 1
 
1.9%
9513.22 1
 
1.9%
13732.63 1
 
1.9%
14229.38 1
 
1.9%
13977.78 1
 
1.9%
9589.86 1
 
1.9%
9570.25 1
 
1.9%
10381.87 1
 
1.9%
10045.54 1
 
1.9%
Other values (42) 42
80.8%
ValueCountFrequency (%)
1155.91 1
1.9%
1206.38 1
1.9%
1230.58 1
1.9%
1283.81 1
1.9%
1358.47 1
1.9%
1930.9 1
1.9%
2049.12 1
1.9%
2452.17 1
1.9%
2643.67 1
1.9%
2834.48 1
1.9%
ValueCountFrequency (%)
14229.38 1
1.9%
13998.33 1
1.9%
13977.78 1
1.9%
13813.27 1
1.9%
13774.06 1
1.9%
13732.63 1
1.9%
13381.76 1
1.9%
13228.82 1
1.9%
13216.31 1
1.9%
12876.7 1
1.9%

Hydroenergy
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing52
Missing (%)100.0%
Memory size548.0 B

Nuclear
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing51
Missing (%)98.1%
Memory size548.0 B
402.79

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters6
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row402.79

Common Values

ValueCountFrequency (%)
402.79 1
 
1.9%
(Missing) 51
98.1%

Length

2023-07-30T07:29:27.600449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-30T07:29:27.803523image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
402.79 1
100.0%

Most occurring characters

ValueCountFrequency (%)
4 1
16.7%
0 1
16.7%
2 1
16.7%
. 1
16.7%
7 1
16.7%
9 1
16.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5
83.3%
Other Punctuation 1
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 1
20.0%
0 1
20.0%
2 1
20.0%
7 1
20.0%
9 1
20.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 1
16.7%
0 1
16.7%
2 1
16.7%
. 1
16.7%
7 1
16.7%
9 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 1
16.7%
0 1
16.7%
2 1
16.7%
. 1
16.7%
7 1
16.7%
9 1
16.7%

Firewood
Categorical

Distinct4
Distinct (%)66.7%
Missing46
Missing (%)88.5%
Memory size548.0 B
3.76
0.94
1.57
0.63

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters24
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)50.0%

Sample

1st row0.94
2nd row1.57
3rd row0.63
4th row3.76
5th row3.76

Common Values

ValueCountFrequency (%)
3.76 3
 
5.8%
0.94 1
 
1.9%
1.57 1
 
1.9%
0.63 1
 
1.9%
(Missing) 46
88.5%

Length

2023-07-30T07:29:27.963463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-30T07:29:28.190179image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3.76 3
50.0%
0.94 1
 
16.7%
1.57 1
 
16.7%
0.63 1
 
16.7%

Most occurring characters

ValueCountFrequency (%)
. 6
25.0%
3 4
16.7%
7 4
16.7%
6 4
16.7%
0 2
 
8.3%
9 1
 
4.2%
4 1
 
4.2%
1 1
 
4.2%
5 1
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 18
75.0%
Other Punctuation 6
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 4
22.2%
7 4
22.2%
6 4
22.2%
0 2
11.1%
9 1
 
5.6%
4 1
 
5.6%
1 1
 
5.6%
5 1
 
5.6%
Other Punctuation
ValueCountFrequency (%)
. 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 24
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 6
25.0%
3 4
16.7%
7 4
16.7%
6 4
16.7%
0 2
 
8.3%
9 1
 
4.2%
4 1
 
4.2%
1 1
 
4.2%
5 1
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 6
25.0%
3 4
16.7%
7 4
16.7%
6 4
16.7%
0 2
 
8.3%
9 1
 
4.2%
4 1
 
4.2%
1 1
 
4.2%
5 1
 
4.2%

Sugarcane and products
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing52
Missing (%)100.0%
Memory size548.0 B

Other Primary_x000d_
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing52
Missing (%)100.0%
Memory size548.0 B

Total Primaries
Real number (ℝ)

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38332.941
Minimum19203.53
Maximum53940.61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size548.0 B
2023-07-30T07:29:28.432551image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum19203.53
5-th percentile27513.1
Q135843.115
median38742.69
Q341017.645
95-th percentile47946.232
Maximum53940.61
Range34737.08
Interquartile range (IQR)5174.53

Descriptive statistics

Standard deviation6428.9999
Coefficient of variation (CV)0.16771476
Kurtosis1.4512042
Mean38332.941
Median Absolute Deviation (MAD)2637.22
Skewness-0.54496711
Sum1993312.9
Variance41332040
MonotonicityNot monotonic
2023-07-30T07:29:28.692012image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19203.53 1
 
1.9%
21590.47 1
 
1.9%
38793.58 1
 
1.9%
38530.48 1
 
1.9%
36849.66 1
 
1.9%
39715.03 1
 
1.9%
34425 1
 
1.9%
32513.8 1
 
1.9%
40881.78 1
 
1.9%
36341.91 1
 
1.9%
Other values (42) 42
80.8%
ValueCountFrequency (%)
19203.53 1
1.9%
21590.47 1
1.9%
26990 1
1.9%
27941.09 1
1.9%
30035.76 1
1.9%
31015.53 1
1.9%
31303.81 1
1.9%
32513.8 1
1.9%
32851.58 1
1.9%
33572.38 1
1.9%
ValueCountFrequency (%)
53940.61 1
1.9%
48702.88 1
1.9%
48262.84 1
1.9%
47687.19 1
1.9%
47432.63 1
1.9%
46211.04 1
1.9%
45001.41 1
1.9%
44181.28 1
1.9%
44120.84 1
1.9%
43816.49 1
1.9%

Electricity
Real number (ℝ)

Distinct45
Distinct (%)97.8%
Missing6
Missing (%)11.5%
Infinite0
Infinite (%)0.0%
Mean2255.6511
Minimum0.17
Maximum3812.14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size548.0 B
2023-07-30T07:29:28.961311image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.17
5-th percentile1.375
Q11469.7975
median2943.525
Q33346.48
95-th percentile3552.7075
Maximum3812.14
Range3811.97
Interquartile range (IQR)1876.6825

Descriptive statistics

Standard deviation1347.917
Coefficient of variation (CV)0.59757334
Kurtosis-0.94760647
Mean2255.6511
Median Absolute Deviation (MAD)606.31
Skewness-0.78292788
Sum103759.95
Variance1816880.1
MonotonicityNot monotonic
2023-07-30T07:29:29.215029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
6.79 2
 
3.8%
3296.8 1
 
1.9%
3137.26 1
 
1.9%
3186.23 1
 
1.9%
3207.74 1
 
1.9%
3363.04 1
 
1.9%
3555.58 1
 
1.9%
3505.77 1
 
1.9%
3680.29 1
 
1.9%
3495.47 1
 
1.9%
Other values (35) 35
67.3%
(Missing) 6
 
11.5%
ValueCountFrequency (%)
0.17 1
1.9%
0.77 1
1.9%
1.29 1
1.9%
1.63 1
1.9%
4.81 1
1.9%
5.24 1
1.9%
6.36 1
1.9%
6.79 2
3.8%
164.88 1
1.9%
885.53 1
1.9%
ValueCountFrequency (%)
3812.14 1
1.9%
3680.29 1
1.9%
3555.58 1
1.9%
3544.09 1
1.9%
3505.77 1
1.9%
3503.83 1
1.9%
3495.47 1
1.9%
3471.57 1
1.9%
3470.48 1
1.9%
3435.87 1
1.9%

LPG
Real number (ℝ)

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1395.9479
Minimum33.79
Maximum3210.52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size548.0 B
2023-07-30T07:29:29.475521image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum33.79
5-th percentile116.582
Q1520.3475
median1449.88
Q32083.515
95-th percentile2969.5435
Maximum3210.52
Range3176.73
Interquartile range (IQR)1563.1675

Descriptive statistics

Standard deviation929.59621
Coefficient of variation (CV)0.66592473
Kurtosis-1.1016936
Mean1395.9479
Median Absolute Deviation (MAD)778.73
Skewness0.10097406
Sum72589.29
Variance864149.12
MonotonicityNot monotonic
2023-07-30T07:29:29.724331image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
415.27 1
 
1.9%
430.63 1
 
1.9%
3125.38 1
 
1.9%
3210.52 1
 
1.9%
3034.02 1
 
1.9%
2286.42 1
 
1.9%
2076.1 1
 
1.9%
1263.12 1
 
1.9%
1243.45 1
 
1.9%
577.55 1
 
1.9%
Other values (42) 42
80.8%
ValueCountFrequency (%)
33.79 1
1.9%
71.26 1
1.9%
84.77 1
1.9%
142.61 1
1.9%
144.46 1
1.9%
152.35 1
1.9%
182.45 1
1.9%
191.05 1
1.9%
243.26 1
1.9%
276.09 1
1.9%
ValueCountFrequency (%)
3210.52 1
1.9%
3125.38 1
1.9%
3034.02 1
1.9%
2916.79 1
1.9%
2670.66 1
1.9%
2541.65 1
1.9%
2448.38 1
1.9%
2286.42 1
1.9%
2276.47 1
1.9%
2229.07 1
1.9%

Gasoline/alcohol
Real number (ℝ)

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3255.0881
Minimum39.42
Maximum12559.71
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size548.0 B
2023-07-30T07:29:29.985648image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum39.42
5-th percentile62.867
Q193.2
median2828.31
Q34518.8125
95-th percentile9723.182
Maximum12559.71
Range12520.29
Interquartile range (IQR)4425.6125

Descriptive statistics

Standard deviation3455.6154
Coefficient of variation (CV)1.0616043
Kurtosis-0.0073259933
Mean3255.0881
Median Absolute Deviation (MAD)2719.35
Skewness0.95944692
Sum169264.58
Variance11941278
MonotonicityNot monotonic
2023-07-30T07:29:30.254630image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
72.68 1
 
1.9%
83.87 1
 
1.9%
4065.49 1
 
1.9%
3303.3 1
 
1.9%
2960.18 1
 
1.9%
2808.05 1
 
1.9%
2606.02 1
 
1.9%
2580.15 1
 
1.9%
3478.15 1
 
1.9%
3736.81 1
 
1.9%
Other values (42) 42
80.8%
ValueCountFrequency (%)
39.42 1
1.9%
61.5 1
1.9%
62.02 1
1.9%
63.56 1
1.9%
64.3 1
1.9%
66.25 1
1.9%
67.79 1
1.9%
68.49 1
1.9%
72.68 1
1.9%
72.97 1
1.9%
ValueCountFrequency (%)
12559.71 1
1.9%
11114.11 1
1.9%
9973.85 1
1.9%
9518.09 1
1.9%
8841.39 1
1.9%
8565.31 1
1.9%
8383.05 1
1.9%
7724.44 1
1.9%
7356.32 1
1.9%
7230.56 1
1.9%

Kerosene/jet fuel
Real number (ℝ)

Distinct41
Distinct (%)100.0%
Missing11
Missing (%)21.2%
Infinite0
Infinite (%)0.0%
Mean573.44878
Minimum4.91
Maximum1576.62
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size548.0 B
2023-07-30T07:29:30.493427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum4.91
5-th percentile19.65
Q188.56
median558.86
Q3850.92
95-th percentile1478.39
Maximum1576.62
Range1571.71
Interquartile range (IQR)762.36

Descriptive statistics

Standard deviation488.70543
Coefficient of variation (CV)0.85222158
Kurtosis-0.89106738
Mean573.44878
Median Absolute Deviation (MAD)433.71
Skewness0.48861235
Sum23511.4
Variance238832.99
MonotonicityNot monotonic
2023-07-30T07:29:30.737900image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
8.19 1
 
1.9%
1502.42 1
 
1.9%
88.56 1
 
1.9%
266.08 1
 
1.9%
574.79 1
 
1.9%
730.79 1
 
1.9%
1227.41 1
 
1.9%
1041.02 1
 
1.9%
1576.62 1
 
1.9%
1478.39 1
 
1.9%
Other values (31) 31
59.6%
(Missing) 11
 
21.2%
ValueCountFrequency (%)
4.91 1
1.9%
8.19 1
1.9%
19.65 1
1.9%
24.34 1
1.9%
24.56 1
1.9%
28.81 1
1.9%
29.02 1
1.9%
37.71 1
1.9%
38.05 1
1.9%
45.85 1
1.9%
ValueCountFrequency (%)
1576.62 1
1.9%
1502.42 1
1.9%
1478.39 1
1.9%
1432.68 1
1.9%
1227.41 1
1.9%
1207.39 1
1.9%
1103.29 1
1.9%
1081.55 1
1.9%
1041.02 1
1.9%
886.15 1
1.9%

Diesel oil
Real number (ℝ)

Distinct50
Distinct (%)100.0%
Missing2
Missing (%)3.8%
Infinite0
Infinite (%)0.0%
Mean3756.9456
Minimum1.74
Maximum12211.92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size548.0 B
2023-07-30T07:29:30.988503image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.74
5-th percentile69.0165
Q1483.3375
median2986.02
Q35671.2425
95-th percentile10593.106
Maximum12211.92
Range12210.18
Interquartile range (IQR)5187.905

Descriptive statistics

Standard deviation3586.3843
Coefficient of variation (CV)0.95460108
Kurtosis-0.54213779
Mean3756.9456
Median Absolute Deviation (MAD)2561.965
Skewness0.74707509
Sum187847.28
Variance12862152
MonotonicityNot monotonic
2023-07-30T07:29:31.262440image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2973.33 1
 
1.9%
4613.42 1
 
1.9%
5139.49 1
 
1.9%
5759.23 1
 
1.9%
5407.28 1
 
1.9%
3233.02 1
 
1.9%
2279.87 1
 
1.9%
2005.88 1
 
1.9%
2998.71 1
 
1.9%
4313.57 1
 
1.9%
Other values (40) 40
76.9%
(Missing) 2
 
3.8%
ValueCountFrequency (%)
1.74 1
1.9%
60.01 1
1.9%
65.16 1
1.9%
73.73 1
1.9%
85.73 1
1.9%
140.6 1
1.9%
153.46 1
1.9%
192.04 1
1.9%
206.78 1
1.9%
214.33 1
1.9%
ValueCountFrequency (%)
12211.92 1
1.9%
11003.23 1
1.9%
10958.79 1
1.9%
10146.16 1
1.9%
9854.67 1
1.9%
9546.02 1
1.9%
8487.12 1
1.9%
8228.29 1
1.9%
7731.84 1
1.9%
7461.76 1
1.9%

Fuel oil
Real number (ℝ)

Distinct51
Distinct (%)100.0%
Missing1
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean517.61333
Minimum9.76
Maximum4740.59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size548.0 B
2023-07-30T07:29:31.513847image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum9.76
5-th percentile29.71
Q184.985
median223.98
Q3559.18
95-th percentile1909.615
Maximum4740.59
Range4730.83
Interquartile range (IQR)474.195

Descriptive statistics

Standard deviation816.27505
Coefficient of variation (CV)1.5769977
Kurtosis14.598326
Mean517.61333
Median Absolute Deviation (MAD)166.7
Skewness3.4668061
Sum26398.28
Variance666304.96
MonotonicityNot monotonic
2023-07-30T07:29:31.768654image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
411.45 1
 
1.9%
56.77 1
 
1.9%
217.7 1
 
1.9%
64.94 1
 
1.9%
11.89 1
 
1.9%
57.28 1
 
1.9%
90.29 1
 
1.9%
124.11 1
 
1.9%
50.6 1
 
1.9%
240.78 1
 
1.9%
Other values (41) 41
78.8%
ValueCountFrequency (%)
9.76 1
1.9%
11.89 1
1.9%
23.01 1
1.9%
36.41 1
1.9%
50.6 1
1.9%
53.65 1
1.9%
56.77 1
1.9%
57.28 1
1.9%
60.89 1
1.9%
64.94 1
1.9%
ValueCountFrequency (%)
4740.59 1
1.9%
2673.01 1
1.9%
2067.16 1
1.9%
1752.07 1
1.9%
1598.43 1
1.9%
1133.45 1
1.9%
1017.88 1
1.9%
861.22 1
1.9%
765.93 1
1.9%
680.54 1
1.9%

Coke
Real number (ℝ)

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean758.17115
Minimum41.38
Maximum1853.49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size548.0 B
2023-07-30T07:29:32.040164image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum41.38
5-th percentile69.936
Q1181.9125
median833.48
Q31184.5975
95-th percentile1468.488
Maximum1853.49
Range1812.11
Interquartile range (IQR)1002.685

Descriptive statistics

Standard deviation517.64639
Coefficient of variation (CV)0.68275664
Kurtosis-1.3061921
Mean758.17115
Median Absolute Deviation (MAD)433.76
Skewness-0.02327803
Sum39424.9
Variance267957.79
MonotonicityNot monotonic
2023-07-30T07:29:32.301564image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
72.42 1
 
1.9%
82.08 1
 
1.9%
1182.61 1
 
1.9%
799.95 1
 
1.9%
1118.41 1
 
1.9%
1122.57 1
 
1.9%
1463.52 1
 
1.9%
1853.49 1
 
1.9%
1437.41 1
 
1.9%
1130.51 1
 
1.9%
Other values (42) 42
80.8%
ValueCountFrequency (%)
41.38 1
1.9%
64.14 1
1.9%
66.9 1
1.9%
72.42 1
1.9%
80.01 1
1.9%
82.08 1
1.9%
84.84 1
1.9%
95.87 1
1.9%
101.39 1
1.9%
120.01 1
1.9%
ValueCountFrequency (%)
1853.49 1
1.9%
1579.76 1
1.9%
1474.56 1
1.9%
1463.52 1
1.9%
1437.41 1
1.9%
1307.75 1
1.9%
1305.11 1
1.9%
1265.76 1
1.9%
1251.42 1
1.9%
1239.87 1
1.9%

Charcoal
Real number (ℝ)

Distinct16
Distinct (%)88.9%
Missing34
Missing (%)65.4%
Infinite0
Infinite (%)0.0%
Mean16.320556
Minimum0.45
Maximum101.81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size548.0 B
2023-07-30T07:29:32.532068image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.45
5-th percentile0.6115
Q14.595
median7.525
Q312.3775
95-th percentile64.733
Maximum101.81
Range101.36
Interquartile range (IQR)7.7825

Descriptive statistics

Standard deviation25.508846
Coefficient of variation (CV)1.5629888
Kurtosis7.6473365
Mean16.320556
Median Absolute Deviation (MAD)4.5
Skewness2.7161523
Sum293.77
Variance650.70122
MonotonicityNot monotonic
2023-07-30T07:29:32.734904image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
7.27 2
 
3.8%
0.64 2
 
3.8%
11.32 1
 
1.9%
5.66 1
 
1.9%
4.24 1
 
1.9%
7.07 1
 
1.9%
7.78 1
 
1.9%
1.41 1
 
1.9%
7.93 1
 
1.9%
16.51 1
 
1.9%
Other values (6) 6
 
11.5%
(Missing) 34
65.4%
ValueCountFrequency (%)
0.45 1
1.9%
0.64 2
3.8%
1.41 1
1.9%
4.24 1
1.9%
5.66 1
1.9%
7.07 1
1.9%
7.27 2
3.8%
7.78 1
1.9%
7.93 1
1.9%
9.34 1
1.9%
ValueCountFrequency (%)
101.81 1
1.9%
58.19 1
1.9%
33.51 1
1.9%
16.51 1
1.9%
12.73 1
1.9%
11.32 1
1.9%
9.34 1
1.9%
7.93 1
1.9%
7.78 1
1.9%
7.27 2
3.8%

Gases
Categorical

Distinct3
Distinct (%)25.0%
Missing40
Missing (%)76.9%
Memory size548.0 B
0.13
10 
0.17
 
1
8.33
 
1

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters48
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)16.7%

Sample

1st row0.17
2nd row8.33
3rd row0.13
4th row0.13
5th row0.13

Common Values

ValueCountFrequency (%)
0.13 10
 
19.2%
0.17 1
 
1.9%
8.33 1
 
1.9%
(Missing) 40
76.9%

Length

2023-07-30T07:29:32.940831image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-30T07:29:33.159531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.13 10
83.3%
0.17 1
 
8.3%
8.33 1
 
8.3%

Most occurring characters

ValueCountFrequency (%)
. 12
25.0%
3 12
25.0%
0 11
22.9%
1 11
22.9%
7 1
 
2.1%
8 1
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 36
75.0%
Other Punctuation 12
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 12
33.3%
0 11
30.6%
1 11
30.6%
7 1
 
2.8%
8 1
 
2.8%
Other Punctuation
ValueCountFrequency (%)
. 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 48
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 12
25.0%
3 12
25.0%
0 11
22.9%
1 11
22.9%
7 1
 
2.1%
8 1
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 48
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 12
25.0%
3 12
25.0%
0 11
22.9%
1 11
22.9%
7 1
 
2.1%
8 1
 
2.1%

Other secondary
Real number (ℝ)

Distinct35
Distinct (%)100.0%
Missing17
Missing (%)32.7%
Infinite0
Infinite (%)0.0%
Mean1731.276
Minimum9.72
Maximum3873.44
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size548.0 B
2023-07-30T07:29:33.366426image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum9.72
5-th percentile46.55
Q1211.21
median1996
Q32662.465
95-th percentile3358.786
Maximum3873.44
Range3863.72
Interquartile range (IQR)2451.255

Descriptive statistics

Standard deviation1241.3723
Coefficient of variation (CV)0.71702736
Kurtosis-1.3515077
Mean1731.276
Median Absolute Deviation (MAD)1093.83
Skewness-0.20290056
Sum60594.66
Variance1541005.1
MonotonicityNot monotonic
2023-07-30T07:29:33.604414image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
120.06 1
 
1.9%
2252.3 1
 
1.9%
2736.31 1
 
1.9%
3089.83 1
 
1.9%
2861.89 1
 
1.9%
3387.57 1
 
1.9%
3873.44 1
 
1.9%
3233.54 1
 
1.9%
3288.87 1
 
1.9%
3346.45 1
 
1.9%
Other values (25) 25
48.1%
(Missing) 17
32.7%
ValueCountFrequency (%)
9.72 1
1.9%
13.09 1
1.9%
60.89 1
1.9%
72.42 1
1.9%
86.58 1
1.9%
113.74 1
1.9%
120.06 1
1.9%
122.69 1
1.9%
134.96 1
1.9%
287.46 1
1.9%
ValueCountFrequency (%)
3873.44 1
1.9%
3387.57 1
1.9%
3346.45 1
1.9%
3288.87 1
1.9%
3233.54 1
1.9%
3089.83 1
1.9%
2901.18 1
1.9%
2861.89 1
1.9%
2736.31 1
1.9%
2588.62 1
1.9%

Non-energy
Real number (ℝ)

Distinct51
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean552.43769
Minimum11.55
Maximum1724.21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size548.0 B
2023-07-30T07:29:35.275422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum11.55
5-th percentile56.135
Q1132.8575
median350.525
Q31048.0675
95-th percentile1334.3695
Maximum1724.21
Range1712.66
Interquartile range (IQR)915.21

Descriptive statistics

Standard deviation491.63461
Coefficient of variation (CV)0.88993676
Kurtosis-0.67260167
Mean552.43769
Median Absolute Deviation (MAD)252.46
Skewness0.79811953
Sum28726.76
Variance241704.59
MonotonicityNot monotonic
2023-07-30T07:29:35.546274image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
470.78 2
 
3.8%
368.92 1
 
1.9%
969.15 1
 
1.9%
237.73 1
 
1.9%
156.62 1
 
1.9%
233.54 1
 
1.9%
424.3 1
 
1.9%
432.57 1
 
1.9%
485.03 1
 
1.9%
1039.76 1
 
1.9%
Other values (41) 41
78.8%
ValueCountFrequency (%)
11.55 1
1.9%
49.37 1
1.9%
55.64 1
1.9%
56.54 1
1.9%
67.14 1
1.9%
76.57 1
1.9%
82.77 1
1.9%
90.11 1
1.9%
97.95 1
1.9%
98.18 1
1.9%
ValueCountFrequency (%)
1724.21 1
1.9%
1712.36 1
1.9%
1380.63 1
1.9%
1296.52 1
1.9%
1244.1 1
1.9%
1236.71 1
1.9%
1161.12 1
1.9%
1143.17 1
1.9%
1136.93 1
1.9%
1134.03 1
1.9%

Total Secundaries
Real number (ℝ)

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13700.4
Minimum643.69
Maximum33337.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size548.0 B
2023-07-30T07:29:35.876366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum643.69
5-th percentile1066.0095
Q11767.0375
median15168.515
Q319439.458
95-th percentile31012.43
Maximum33337.95
Range32694.26
Interquartile range (IQR)17672.42

Descriptive statistics

Standard deviation10848.144
Coefficient of variation (CV)0.79181224
Kurtosis-1.3329985
Mean13700.4
Median Absolute Deviation (MAD)11378.07
Skewness0.24877806
Sum712420.78
Variance1.1768223 × 108
MonotonicityNot monotonic
2023-07-30T07:29:36.335857image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
937.48 1
 
1.9%
1657.98 1
 
1.9%
18952.68 1
 
1.9%
18419.31 1
 
1.9%
19007.16 1
 
1.9%
19036.86 1
 
1.9%
17910.79 1
 
1.9%
15122.92 1
 
1.9%
14530.64 1
 
1.9%
14224.59 1
 
1.9%
Other values (42) 42
80.8%
ValueCountFrequency (%)
643.69 1
1.9%
655.64 1
1.9%
937.48 1
1.9%
1171.17 1
1.9%
1219.95 1
1.9%
1271.06 1
1.9%
1286.62 1
1.9%
1495.32 1
1.9%
1525.18 1
1.9%
1618.64 1
1.9%
ValueCountFrequency (%)
33337.95 1
1.9%
31681.31 1
1.9%
31674.9 1
1.9%
30470.41 1
1.9%
29397.07 1
1.9%
29385.89 1
1.9%
29112.94 1
1.9%
28254.35 1
1.9%
27214.13 1
1.9%
26411.84 1
1.9%

Total
Real number (ℝ)

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52033.34
Minimum20141.01
Maximum78099.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size548.0 B
2023-07-30T07:29:36.786643image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum20141.01
5-th percentile32181.148
Q145352.698
median52731.965
Q359291.618
95-th percentile70629.802
Maximum78099.95
Range57958.94
Interquartile range (IQR)13938.92

Descriptive statistics

Standard deviation12214.192
Coefficient of variation (CV)0.2347378
Kurtosis0.35007955
Mean52033.34
Median Absolute Deviation (MAD)7318.455
Skewness-0.30664286
Sum2705733.7
Variance1.4918648 × 108
MonotonicityNot monotonic
2023-07-30T07:29:37.246164image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20141.01 1
 
1.9%
23248.45 1
 
1.9%
57746.26 1
 
1.9%
56949.8 1
 
1.9%
55856.82 1
 
1.9%
58751.89 1
 
1.9%
52335.79 1
 
1.9%
47636.72 1
 
1.9%
55412.43 1
 
1.9%
50566.51 1
 
1.9%
Other values (42) 42
80.8%
ValueCountFrequency (%)
20141.01 1
1.9%
23248.45 1
1.9%
28276.61 1
1.9%
35375.77 1
1.9%
37876.93 1
1.9%
37908.01 1
1.9%
39103.07 1
1.9%
39280.52 1
1.9%
39643.11 1
1.9%
42658.78 1
1.9%
ValueCountFrequency (%)
78099.95 1
1.9%
75941.54 1
1.9%
71413.25 1
1.9%
69988.8 1
1.9%
68077.69 1
1.9%
67046.66 1
1.9%
65719.13 1
1.9%
64353.48 1
1.9%
63321.99 1
1.9%
61717.06 1
1.9%

Interactions

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2023-07-30T07:28:21.927127image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:28:25.345019image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:28:28.966729image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:28:33.798727image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:28:37.522086image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:28:41.153873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:28:45.865998image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:28:50.655859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:28:54.268947image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:28:59.033920image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:02.503196image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:05.742515image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:10.078062image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:14.452914image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:18.052219image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:21.771381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:28:17.662320image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:28:22.115230image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:28:25.542906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:28:29.312101image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:28:34.014850image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:28:37.729583image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:28:41.357147image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:28:46.060102image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:28:50.853163image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:28:54.490624image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:28:59.241466image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:02.693316image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:05.953879image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:10.376004image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:14.639045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:18.264127image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:21.967009image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:28:18.014658image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:28:22.307853image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:28:25.736670image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:28:29.575359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:28:34.194842image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:28:37.919881image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:28:41.527357image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:28:46.241014image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:28:51.055686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:28:54.669824image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:28:59.415735image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:02.892809image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:06.115763image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:10.608263image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:14.819398image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:18.458098image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:22.174457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:28:18.477206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:28:22.507714image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:28:25.917521image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:28:29.921327image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:28:34.403435image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:28:38.144925image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:28:41.730080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:28:46.466521image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:28:51.277418image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:28:54.879166image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:28:59.610560image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:03.099001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:06.314654image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:10.923103image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:15.026849image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:18.652727image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:22.400639image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:28:18.943127image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:28:22.706887image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:28:26.132309image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:28:30.227447image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:28:34.609189image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:28:38.361560image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:28:41.925768image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:28:47.825324image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:28:51.487533image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:28:55.081793image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:28:59.804922image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:03.301580image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:06.503118image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:11.251552image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:15.239882image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:18.850602image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:22.641588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:28:19.274556image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:28:22.921694image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:28:26.337616image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:28:30.517437image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:28:34.817142image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:28:38.566485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:28:42.132785image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:28:48.019037image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:28:51.685268image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:28:55.274674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:28:59.985225image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:03.492146image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:06.690932image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:11.535078image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:15.426320image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:29:19.045337image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Missing values

2023-07-30T07:29:23.170549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-30T07:29:24.233981image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-07-30T07:29:24.932987image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

59YearOilNatural gasCoalHydroenergyNuclearFirewoodSugarcane and productsOther Primary_x000d_Total PrimariesElectricityLPGGasoline/alcoholKerosene/jet fuelDiesel oilFuel oilCokeCharcoalGasesOther secondaryNon-energyTotal SecundariesTotal
1197017845.05NaN1358.47NaNNaNNaNNaNNaN19203.53NaN415.2772.688.19NaNNaN72.42NaNNaNNaN368.92937.4820141.01
2197120434.56NaN1155.91NaNNaNNaNNaNNaN21590.47NaN430.6383.8719.65192.04411.4582.08NaNNaNNaN438.281657.9823248.45
3197225706.19NaN1283.81NaNNaNNaNNaNNaN26990.00NaN458.27126.0224.56NaN80.08126.91NaNNaNNaN470.781286.6228276.61
4197335000.20NaN1230.58NaNNaNNaNNaNNaN36230.780.77182.45297.3745.85214.33338.7395.87NaNNaNNaN470.781646.1537876.93
5197435077.19NaN1206.38NaNNaNNaNNaNNaN36283.575.24243.26575.26NaN60.0169.03142.77NaNNaNNaN528.851624.4437908.01
6197536528.48NaN1930.90NaNNaNNaNNaNNaN38459.386.7933.7961.50NaN85.7323.01120.01NaNNaNNaN312.85643.6939103.07
7197641767.38NaN2049.12NaNNaNNaNNaNNaN43816.496.36152.3577.58NaN153.46765.9384.84NaNNaNNaN378.131618.6445435.13
8197741477.17NaN2643.67NaNNaNNaNNaNNaN44120.841.63191.0568.49NaN65.16536.62101.39NaNNaNNaN306.721271.0645391.89
9197845810.67NaN2452.17NaNNaNNaNNaNNaN48262.844.8171.2667.794.9173.73165.40331.07NaNNaN120.06332.131171.1749434.01
10197951000.36NaN2940.25NaNNaNNaNNaNNaN53940.616.7984.77118.74NaN140.60227.43226.23NaNNaN134.96280.431219.9555160.56
59YearOilNatural gasCoalHydroenergyNuclearFirewoodSugarcane and productsOther Primary_x000d_Total PrimariesElectricityLPGGasoline/alcoholKerosene/jet fuelDiesel oilFuel oilCokeCharcoalGasesOther secondaryNon-energyTotal SecundariesTotal
43201218170.3911570.4211135.05NaNNaNNaNNaNNaN40875.863503.831743.748565.311502.428228.29212.121095.16NaN0.133233.541028.3929112.9469988.80
44201320778.7614885.5512022.88NaNNaNNaNNaNNaN47687.193470.482060.177055.781432.688487.1289.891305.11NaN0.133288.871064.1228254.3575941.54
45201418362.1716958.9613381.76NaNNaNNaNNaNNaN48702.882906.352276.477356.321207.399546.02372.481251.42NaN0.133346.451134.0329397.0778099.95
46201515614.6416157.9513228.82NaNNaNNaNNaNNaN45001.412980.701949.758841.391103.295875.80331.281579.76NaN0.132588.621161.1226411.8471413.25
4720168132.5510294.5612876.70NaNNaNNaNNaNNaN31303.813544.092105.769973.85768.267164.1860.89761.94NaN0.131792.041042.9927214.1358517.94
4820177606.959410.2513998.33NaNNaNNaNNaNNaN31015.533132.152006.8812559.71472.1310958.7971.711058.73NaN0.131940.791136.9333337.9564353.48
4920189776.219301.3113774.06NaNNaNNaNNaNNaN32851.583000.792125.169518.09735.169854.67304.691265.76NaN0.132421.851244.1030470.4163321.99
5020199022.908606.6912406.16NaNNaNNaNNaNNaN30035.762158.062166.9311114.11850.9211003.2353.65899.43NaN0.132291.681143.1731681.3161717.06
5120208818.868436.8510685.38NaNNaNNaNNaNNaN27941.092154.362229.077230.56262.5110146.1636.41934.43NaNNaN2059.141236.7126289.3554230.44
5220217359.2114796.2313216.31NaNNaNNaNNaNNaN35371.751985.702448.388383.05300.4112211.92581.741150.16NaNNaN2901.181712.3631674.9067046.66